Preoperative anemia is an unsuspecting driver of machine learning prediction of adverse outcomes after lumbar spinal fusion.

BACKGROUND CONTEXT: Preoperative risk assessment remains a challenge in spinal fusion operations. Predictive modeling provides data-driven estimates of postsurgical outcomes, guiding clinical decisions and improving patient care. Moreover, automated machine learning models are both effective and user-friendly, allowing healthcare professionals with minimal technical expertise to identify high-risk patients who may need additional preoperative support.

PURPOSE: This study investigated the use of automated machine learning models to predict discharge disposition, length of hospital stay, and readmission postsurgery by analyzing preoperative patient electronic medical record data and identifying key factors influencing adverse outcomes.

STUDY DESIGN/

SETTING

Retrospective cohort study.

PATIENT SAMPLE: The sample includes electronic medical records of 3,006 unique surgical events from 2,855 patients who underwent lumbar spinal fusion surgeries at a single institution.

OUTCOME MEASURES: The adverse outcomes assessed were discharge disposition (nonhome facility), length of hospital stay (extended stay), and readmission within 90 days postsurgery.

METHODS

We employed several inferential and predictive approaches, including the automated machine learning tool TPOT2 (Tree-based Pipeline Optimization Tool-2). TPOT2, which uses genetic programming to select optimal machine learning pipelines in a process inspired by molecular evolution, constructed, optimized and identified robust predictive models for all outcomes. Feature importance values were derived to identify major preoperative predictive features driving optimal models.

RESULTS

Adverse outcome rates were 25.9% for discharge to nonhome facilities, 23.9% for extended hospital stay, and 24.7% for readmission within 90 days postsurgery. TPOT2 delivered the best-performing predictive models, achieving balanced accuracies ([Sensitivity {true positive rate} + Specificity {true negative rate}]) / 2) of 0.72 for discharge disposition, 0.72 for length of stay, and 0.67 for readmission. Notably, preoperative hemoglobin emerged as a consistently strong predictor in best-performing models across outcomes. Patients with severe anemia (hemoglobin <80g/dL) demonstrated higher associations with all adverse outcomes and common comorbidities associated with frailty (eg, hypertension, type II diabetes, and chronic pain). Additional patient variables and comorbidities, including body mass index, age, and mental health status, influencing postsurgical outcomes were also highly predictive.

CONCLUSIONS

This study demonstrates the effectiveness of automated machine learning in predicting postsurgical adverse outcomes and identifying key preoperative predictors associated with such outcomes. While factors like age, BMI, insurance type, and specific comorbidities showed notable effects on outcomes, preoperative hemoglobin consistently emerged as a significant predictor across outcomes, suggesting its critical role in presurgical assessment. These findings underscore the potential of enhancing patient care and preoperative assessment through advanced predictive modeling.